Multimodal Knowledge Systems

Dr. Changmeng Zheng received his Bachelor’s and Master’s degree from South China University of Technology (Guangzhou China), and the PhD degree from the Hong Kong Polytechnic University (Hong Kong SAR). He is currently a Research Assistant Professor with the department of computing, the Hong Kong Polytechnic University, Hong Kong SAR. His research work has been published in refereed journals and conferences such as IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, Neural Networks, ACL, EMNLP, COLING, ACM MM. His research interests are in the areas of multimodal learning and social media analytics, especially the knowledge graph and large language models.Prof. Qing Li received his BEng. Degree from Hunan University (Changsha, China), MSc and PhD degrees from the University of Southern California (Los Angeles, USA), all in computer science. He is currently a Chair Professor at the Hong Kong Polytechnic University, a visiting professor of the Zhejiang University, a guest professor of the University of Science and Technology of China, and an adjunct professor of the Hunan University. His research interests include multi-modal data modeling, multimedia retrieval and management, and e-learning systems. Dr. Li has published over 500 papers in technical journals and international conferences in these areas, and is actively involved in the research community by serving as a journal reviewer, programme committee chair/co-chair, and as an organizer/co-organizer of several international conferences. Currently he serves as the Chairman of the Hong Kong Web Society, a councillor of the Database Society of Chinese Computer Federation, and a Steering Committee member of the international WISE Society. He is a Fellow of IEEE, AAIA, and IET.

• Introduction.- Part I: Foundations.- Introduction to Multimodal Knowledge Systems.- Traditional and Modern Approaches.- Part II: Core Methodologies.- Unified Multimodal Graph Learning.- Hierarchical Cross-Modal Representation.- Bias-Aware Data Augmentation.- Iterative Knowledge Refinement.- Part III: Applications and Beyond.- Real-World Applications.- Future Directions and Ethical Considerations.- Conclusion.